Investigations on surface quality characteristics with multi-response parametric optimization and correlations
This paper presents the parametric optimization on surface quality characteristics (Ra, Rz and Rt) in hard turning of EN31 steel using multilayer coated carbide insert (TiN/TiCN/Al2O3) and also finds correlations. The experiments have been conducted based on Taguchi’s L9 orthogonal array. Multiple l...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2016-06-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016816000612 |
| _version_ | 1819131954764709888 |
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| author | Amlana Panda Ashok Kumar Sahoo Arun Kumar Rout |
| author_facet | Amlana Panda Ashok Kumar Sahoo Arun Kumar Rout |
| author_sort | Amlana Panda |
| collection | DOAJ |
| description | This paper presents the parametric optimization on surface quality characteristics (Ra, Rz and Rt) in hard turning of EN31 steel using multilayer coated carbide insert (TiN/TiCN/Al2O3) and also finds correlations. The experiments have been conducted based on Taguchi’s L9 orthogonal array. Multiple linear regression analysis has been utilized to find the correlations. The integrated multi-response optimization approach using CQL concept in WPCA coupled with Taguchi technique has been implemented. Based on the S/N ratio, the optimal process parameters for surface roughness i.e. Ra and Rz are the depth of cut at level 3 (0.5 mm), the cutting speed at level 3 (140 m/min), and the feed at level 1 (0.04 mm/rev). The optimal process parameters for Rt are found to be the depth of cut at level 3 (0.5 mm), the cutting speed at level 2 (100 m/min), and the feed at level 1 (0.04 mm/rev). Feed and depth of cut are found to be the significant cutting parameters affecting the responses at 95% confidence limit from ANOVA study. The first order model presented high correlation coefficient between the experimental and predicted values. The optimal parametric combination for multi-response (Ra, Rz and Rt) becomes d3–v3–f1 and is greatly improved. |
| first_indexed | 2024-12-22T09:23:43Z |
| format | Article |
| id | doaj.art-d23bfb86d67f4aa1970d460a7c045f62 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-12-22T09:23:43Z |
| publishDate | 2016-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-d23bfb86d67f4aa1970d460a7c045f622022-12-21T18:31:08ZengElsevierAlexandria Engineering Journal1110-01682016-06-015521625163310.1016/j.aej.2016.02.008Investigations on surface quality characteristics with multi-response parametric optimization and correlationsAmlana PandaAshok Kumar SahooArun Kumar RoutThis paper presents the parametric optimization on surface quality characteristics (Ra, Rz and Rt) in hard turning of EN31 steel using multilayer coated carbide insert (TiN/TiCN/Al2O3) and also finds correlations. The experiments have been conducted based on Taguchi’s L9 orthogonal array. Multiple linear regression analysis has been utilized to find the correlations. The integrated multi-response optimization approach using CQL concept in WPCA coupled with Taguchi technique has been implemented. Based on the S/N ratio, the optimal process parameters for surface roughness i.e. Ra and Rz are the depth of cut at level 3 (0.5 mm), the cutting speed at level 3 (140 m/min), and the feed at level 1 (0.04 mm/rev). The optimal process parameters for Rt are found to be the depth of cut at level 3 (0.5 mm), the cutting speed at level 2 (100 m/min), and the feed at level 1 (0.04 mm/rev). Feed and depth of cut are found to be the significant cutting parameters affecting the responses at 95% confidence limit from ANOVA study. The first order model presented high correlation coefficient between the experimental and predicted values. The optimal parametric combination for multi-response (Ra, Rz and Rt) becomes d3–v3–f1 and is greatly improved.http://www.sciencedirect.com/science/article/pii/S1110016816000612Hard machiningMulti-response optimizationMultiple linear regressionWeighted principal component analysisTaguchi method |
| spellingShingle | Amlana Panda Ashok Kumar Sahoo Arun Kumar Rout Investigations on surface quality characteristics with multi-response parametric optimization and correlations Alexandria Engineering Journal Hard machining Multi-response optimization Multiple linear regression Weighted principal component analysis Taguchi method |
| title | Investigations on surface quality characteristics with multi-response parametric optimization and correlations |
| title_full | Investigations on surface quality characteristics with multi-response parametric optimization and correlations |
| title_fullStr | Investigations on surface quality characteristics with multi-response parametric optimization and correlations |
| title_full_unstemmed | Investigations on surface quality characteristics with multi-response parametric optimization and correlations |
| title_short | Investigations on surface quality characteristics with multi-response parametric optimization and correlations |
| title_sort | investigations on surface quality characteristics with multi response parametric optimization and correlations |
| topic | Hard machining Multi-response optimization Multiple linear regression Weighted principal component analysis Taguchi method |
| url | http://www.sciencedirect.com/science/article/pii/S1110016816000612 |
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